I have a matrix X of size n x m. I resized X to a vector a of length n x m.
How can I know "automatically" that the ith element in vector a corresponds to what element position (coordinates) in X?
I have written the following MATLAB code but I do not know how to continue.
X = rand(10,10);
[n,m] = size(X);
a = reshape(X, [n*m, 1]);
t = zeros(length(a),1);
for i = 1 : length(a)
t(i) = % I want to perform here the sum over the x and y coordinate values of the element in X
% that corresponds to the ith element in vector a.
end
Any help will be very appreciated.
That's what ind2sub does:
[row, col] = ind2sub([m n], i);
However, you may prefer to do it manually:
row = mod(i-1,m)+1;
col = floor((i-1)/m)+1;
This works because Matlab used column-major order for storing array elements. For example, in a 3×4 matrix the order in which the elements are stored in memory is as follows:
1 4 7 10
2 5 8 11
3 6 9 12
So the entry in the 2nd row, 3rd column is the 8th element in (column-major) linear order. When this matrix is reshaped into a vector (or into any other shape) this linear order is preserved. Therefore you can retrieve the original coordinates by divisions and modulus operations. Note also that, since Matlab's indexing is 1-based (as opposed to 0-based), the modulus operations need to be shifted by 1.
Related
What is the fastest way of calculating the maximum value, with it's corresponding index, of each 'slice' of a 3D array?
Say you have A with n slices (here I just made each slice 10 by 10, but this can be changed to any size):
A = rand(10,10,n);
You can reshape it to n-columns matrix, then take the maximum of each column:
[val,ind] = max(reshape(A,[],n),[],1);
The first output val will be an n-element vector with all the maximum values, and the second output ind will be their row index in the reshaped A.
Then you get the size of the slices:
sz = size(A);
and use it to find the row (r) and column (c) of each maximum element in each slice:
[r,c] = ind2sub(sz(1:2),ind)
So in this example (using rand and 10x10x6 array for A) you would get something like this at the end (but with different values):
val =
0.99861 0.98895 0.98681 0.99991 0.96057 0.99176
r =
9 7 3 8 2 9
c =
1 1 8 10 10 5
If you have a matrix A with n layers, you can apply max function in two steps to get a 1 x 1 x n matrix with max of each layer
A = rand(10,10,n);
layer_max = max(max(A,[],1),[],2); % 1 x 1 x n matrix, use squeeze to remove extra dims
layer_max = squeeze(layer_max);
This question already has answers here:
Create a zero-filled 2D array with ones at positions indexed by a vector
(4 answers)
Closed 6 years ago.
I have a vector v of size (m,1) whose elements are integers picked from 1:n. I want to create a matrix M of size (m,n) whose elements M(i,j) are 1 if v(i) = j, and are 0 otherwise. I do not want to use loops, and would like to implement this as a simple vector-matrix manipulation only.
So I thought first, to create a matrix with repeated elements
M = v * ones(1,n) % this is a (m,n) matrix of repeated v
For example v=[1,1,3,2]'
m = 4 and n = 3
M =
1 1 1
1 1 1
3 3 3
2 2 2
then I need to create a comparison vector c of size (1,n)
c = 1:n
1 2 3
Then I need to perform a series of logical comparisons
M(1,:)==c % this results in [1,0,0]
.
M(4,:)==c % this results in [0,1,0]
However, I thought it should be possible to perform the last steps of going through each single row in compact matrix notation, but I'm stumped and not knowledgeable enough about indexing.
The end result should be
M =
1 0 0
1 0 0
0 0 1
0 1 0
A very simple call to bsxfun will do the trick:
>> n = 3;
>> v = [1,1,3,2].';
>> M = bsxfun(#eq, v, 1:n)
M =
1 0 0
1 0 0
0 0 1
0 1 0
How the code works is actually quite simple. bsxfun is what is known as the Binary Singleton EXpansion function. What this does is that you provide two arrays / matrices of any size, as long as they are broadcastable. This means that they need to be able to expand in size so that both of them equal in size. In this case, v is your vector of interest and is the first parameter - note that it's transposed. The second parameter is a vector from 1 up to n. What will happen now is the column vector v gets replicated / expands for as many values as there are n and the second vector gets replicated for as many rows as there are in v. We then do an eq / equals operator between these two arrays. This expanded matrix in effect has all 1s in the first column, all 2s in the second column, up until n. By doing an eq between these two matrices, you are in effect determining which values in v are equal to the respective column index.
Here is a detailed time test and breakdown of each function. I placed each implementation into a separate function and I also let n=max(v) so that Luis's first code will work. I used timeit to time each function:
function timing_binary
n = 10000;
v = randi(1000,n,1);
m = numel(v);
function luis_func()
M1 = full(sparse(1:m,v,1));
end
function luis_func2()
%m = numel(v);
%n = 3; %// or compute n automatically as n = max(v);
M2 = zeros(m, n);
M2((1:m).' + (v-1)*m) = 1;
end
function ray_func()
M3 = bsxfun(#eq, v, 1:n);
end
function op_func()
M4= ones(1,m)'*[1:n] == v * ones(1,n);
end
t1 = timeit(#luis_func);
t2 = timeit(#luis_func2);
t3 = timeit(#ray_func);
t4 = timeit(#op_func);
fprintf('Luis Mendo - Sparse: %f\n', t1);
fprintf('Luis Mendo - Indexing: %f\n', t2);
fprintf('rayryeng - bsxfun: %f\n', t3);
fprintf('OP: %f\n', t4);
end
This test assumes n = 10000 and the vector v is a 10000 x 1 vector of randomly distributed integers from 1 up to 1000. BTW, I had to modify Luis's second function so that the indexing will work as the addition requires vectors of compatible dimensions.
Running this code, we get:
>> timing_binary
Luis Mendo - Sparse: 0.015086
Luis Mendo - Indexing: 0.327993
rayryeng - bsxfun: 0.040672
OP: 0.841827
Luis Mendo's sparse code wins (as I expected), followed by bsxfun, followed by indexing and followed by your proposed approach using matrix operations. The timings are in seconds.
Assuming n equals max(v), you can use sparse:
v = [1,1,3,2];
M = full(sparse(1:numel(v),v,1));
What sparse does is build a sparse matrix using the first argument as row indices, the second as column indices, and the third as matrix values. This is then converted into a full matrix with full.
Another approach is to define the matrix containing initially zeros and then use linear indexing to fill in the ones:
v = [1,1,3,2];
m = numel(v);
n = 3; %// or compute n automatically as n = max(v);
M = zeros(m, n);
M((1:m) + (v-1)*m) = 1;
I think I've also found a way to do it, and it would be nice if somebody could tell me which of the methods shown is faster for very large vectors and matrices. The additional method I thought of is the following
M= ones(1,m)'*[1:n] == v * ones(1,n)
Problem from :
https://www.hackerrank.com/contests/epiccode/challenges/white-falcon-and-sequence.
Visit link for references.
I have a sequence of integers (-10^6 to 10^6) A. I need to choose two contiguous disjoint subsequences of A, let's say x and y, of the same size, n.
After that you will calculate the sum given by ∑x(i)y(n−i+1) (1-indexed)
And I have to choose x and y such that sum is maximised.
Eg:
Input:
12
1 7 4 0 9 4 0 1 8 8 2 4
Output: 120
Where x = {4,0,9,4}
y = {8,8,2,4}
∑x(i)y(n−i+1)=4×4+0×2+9×8+4×8=120
Now, the approach that I was thinking of for this is something in lines of O(n^2) which is as follows:
Initialise two variables l = 0 and r = N-1. Here, N is the size of the array.
Now, for l=0, I will calculate the sum while (l<r) which basically refers to the subsequences that will start from the 0th position in the array. Then, I will increment l and decrement r in order to come up with subsequences that start from the above position + 1 and on the right hand side, start from right-1.
Is there any better approach that I can use? Anything more efficient? I thought of sorting but we cannot sort numbers since that will change the order of the numbers.
To answer the question we first define S(i, j) to be the max sum of multlying the two sub-sequence items, for sub-array A[i...j] when the sub-sequence x starts at position i, and sub-sequence y ends on position j.
For example, if A=[1 7 4 0 9 4 0 1 8 8 2 4], then S(1, 2)=1*7=7 and S(2, 5)=7*9+4*0=63.
The recursive rule to compute S is: S(i, j)=max(0, S(i+1, j-1)+A[i]*A[j]), and the end condition is S(i, j)=0 iff i>=j.
The requested final answer is simply the maximum value of S(i, j) for all combinations of i=1..N, j=1..N, since one of the S(i ,j) values will correspond to the max x,y sub-sequences, and thus will be equal the maximum value for the whole array. The complexity of computing all such S(i, j) values is O(N^2) using dynamic programming, since in the course of computing S(i, j) we will also compute the values of up to N other S(i', j') values, but ultimately each combination will be computed only once.
def max_sum(l):
def _max_sub_sum(i, j):
if m[i][j]==None:
v=0
if i<j:
v=max(0, _max_sub_sum(i+1, j-1)+l[i]*l[j])
m[i][j]=v
return m[i][j]
n=len(l)
m=[[None for i in range(n)] for j in range(n)]
v=0
for i in range(n):
for j in range(i, n):
v=max(v, _max_sub_sum(i, j))
return v
WARNING:
This method assumes the numbers are non-negative so this solution does not answer the poster's actual problem now it has been clarified that negative input values are allowed.
Trick 1
Assuming the numbers are always non-negative, it is always best to make the sequences as wide as possible given the location where they meet.
Trick 2
We can change the sum into a standard convolution by summing over all values of i. This produces twice the desired result (as we get both the product of x with y, and y with x), but we can divide by 2 at the end to get the original answer.
Trick 3
You are now attempting to find the maximum of a convolution of a signal with itself. There is a standard method for doing this which is to use the fast fourier transform. Some libraries will have this built in, e.g. in Scipy there is fftconvolve.
Python code
Note that you don't allow the central value to be reused (e.g. for a sequance 1,3,2 we can't make x 1,3 and y 3,1) so we need to examine alternate values of the convolved output.
We can now compute the answer in Python via:
import scipy.signal
A = [1, 7, 4, 0, 9, 4, 0, 1, 8, 8, 2, 4]
print max(scipy.signal.fftconvolve(A,A)[1::2]) / 2
I am writing a function to find a row in matrix which is closest to given vector and given vector can be variable in size. For example if matrix size is by 4 and vector size is 3 then function will check the first three values form each row of matrix.
For comparison, I am calculating the distance of each row in matrix from given vector and then selecting the row which has minimum distance but I think this is not perfect solution because two different row can have same minimum distance from given vector.
I would like to know that is there any built in function available in matlab. I have already tried method ismember.
You can compute distances with pdist2, obtain the minimum, and then find all rows that have minimum distance:
vector = [1 3 2];
matrix = [2 1 2 2
1 3 3 4
0 0 0 0
5 4 3 2]; %// example data
dist = pdist2(vector, matrix(:,1:numel(vector)), 'euclidean'); %// compute distances
mindist = min(dist); %// minimum distance
result = find(dist==mindist); %// minimizing rows
Change 'euclidean' in pdist2 to use other distance definitions.
Depending on your definition of distance you could use bsxfun instead of pdist2. For example, for (squared) Euclidean distance,
dist = sum(bsxfun(#minus, vector, matrix(:,1:numel(vector))).^2, 2); %// squared distance
I have a 3X3 cell array and each element store a (x,y) point.
The point are generate by random number from [0,1].
What I want to do is sort the cell array so that it looks like following
ex: 9 points
each circle is one 2D point
index:(1,1) in the left top corner and (3,3) to the right bottom corner as the usual array index
that is to ensure the topological order.
How do I do it?
Thank in advance.
for the example
pairs = [4 9 2 6 5 1 7 8 3; 9 6 2 1 3 8 7 4 5] (row 1 = x-values, row 2 = y-values))
what I want to do is put them in the cell array so that they can be connected by read lines like the image's topology.
The number of permutations is factorial(9), which is not terribly large. So a brute-froce approach is feasible: test all permutations for your desired conditions, and pick the first that is valid.
In the following I'm using a 2x3x3 array, instead of a 3x3 cell array containing length-2 vectors, because it's much easier that way.
N = 3;
data = rand(2,N,N);
permutations = perms(1:N^2); %// generate all permutations
for k = 1:numel(permutations)
dx = reshape(data(1,permutations(k,:)),N,N); %// permuted x data
dy = reshape(data(2,permutations(k,:)),N,N); %// permuted y data
if all(all(diff(dy,[],1)<0)) && all(all(diff(dx,[],2)>0)) %// solution found
disp(dx) %// display solution: x values
disp(dy) %// y values
break %// we only want one solution
end
end
Note that for some choices of data there may not be a solution.